Machine Learning Training

Machine Learning Training

Machine Learning Training in Jaipur

Artificial intelligence has also given us machine learning as one of its applications. Machine learning helps the systems to learn on their own automatically without putting the effort of programming. The focus of machine learning is only on the development of the computer programs to make it access the database and use the data to learn from it on its own. It is the observation with which the process of learning of machine learning training starts. The observation is made to data, instructions, direct experiences, etc. The dependence of machine learning is on inferences and patterns of database.

Relationship with other subjects

There is a great variety of subjects with which machine learning is used. Machine learning is also called as predictive analysis because it tends to predict the patterns. It shares a close relationship with computational statistics. With the help of computational statistics only, machine learning can make predictions. Machine learning uses the methods that are quite similar to the methods used by data mining and statistics. Machine learning relates with Optimization also. The task of Optimization is to lessen the loss on a training set. The job of machine learning is to tackle the loss on unseen samples.

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The base for machine learning training in Jaipur

There must be strong base of system design and software engineering, statistics and probability, machine learning models and libraries, programming and the fundamentals of computer science. It is also necessary to have good grip over testing, programming, analysis methods of probability, modularity, computer architecture, formal characterization of probability, requirements analysis, data structures, modularity and version control.

Applications of Machine Learning Training

Machine learning is of great use and a number of applications. Kindly note the industries where the applications of machine learning are found: user behavior Analytics, time series forecasting, theorem proving, telecommunication, syntactic pattern recognition, structural health monitoring, speech recognition, software engineering, sequence mining, sentiment analysis, search engines, robot locomotion, recommender systems, optimization, online advertising, natural language understanding, Natural Language Processing, medical diagnosis, marketing, machine translation, etc.

Approaches of machine learning algorithms

The approaches include different kinds of models, types and training models of machine learning algorithms. The different models are decision trees, Bayesian networks, artificial neural networks, support vehicle machines, genetic algorithms, etc. The types of machine learning include unsupervised learning, feature learning, anomaly detection, supervised learning, reinforcement learning, sparse dictionary learning, Association rules, etc. The training model happens to be federated learning.

Coverage of the training program

The best machine learning training institute, Jaipur will give the coverage during the training program of the following topics. The coverage is of an introductory lesson of machine learning and artificial intelligence, machine learning techniques, preprocessing of data, math refresher, regression techniques, classification techniques, clustering in unsupervised learning and other important topics, deep learning and practice projects. You may be given some free courses on Math refresher, data science, python, statistical essential for data science, etc.

Features of training in machine learning

The features of machine learning training institute in Jaipur are here for you. You will be provided with a fixed number of hours for getting trained. You will also be able to enjoy some extra, special sessions by the faculty members or by the industry experts make me understand some important points about the training program. There are different kinds of hands on exercises. As a trainee, you will be getting 24*7 support.

Scope of machine learning engineering

Machine learning training can give you 10 lacs per annum in case you are planning to invest in it. You will be able to have multiple opportunities if you are well trained in hard skills and soft skills both. There are many companies waiting for you. You must study some machine learning software, statistics, and some important tools for better working knowledge, natural language processing and applied math. Around 2.3 million jobs will be generated with the help of artificial intelligence in the area of machine learning by 2020. There can be openings for you like, machine learning scientist, data sciences lead, NLP data scientist, machine learning analyst, machine learning engineer, etc.

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Our Training Program Details

Machine Learning (Regular)
Course:Machine Learning
Certification By:TechieNest, An ISO 9001:2008 Certified Company
Study Material:Book free to each participant (Soft Copy)
FeeINR 9,500/- + Taxes
Duration1 Months/ 50 Hours

Course Details


Machine Learning
DAYTOPICDURATION
Day 1

Machine Learning

  • Introduction to machine learning
  • Understanding the need
  • Understanding Big data and machine learning
  • Running machine learning under Linux platform
  • Introduction to Redhat Enterprise Linux
  • Why Linux is important for machine learning with respect to future
  • Role of Python and R programming in this domain
  • Basic Introduction of Python syntax and programming logic
  • Deep dive with Supervised, Unsupervised and Reinforcement learning
  • Algo discussion with use case
  • Popular machine learning framework like sensor flow , scikit-learn
2 Hours
Day 2

Advance Python programming and its use case

  • Basic of python and why python for machine learning
  • Installation of software and libraries on different OS.
  • Revising python concepts
  • Advance python programming
  • Hands-on with Python standard libraries
  • GITHUB exposure
2 Hours
Day 3Data Science Libraries

  • Understanding & use of Various Open source libraries
  • Importing various modules with different methods
  • File handling with Python
  • Working with Numpy
  • Data types and its various Numerical operations
  • Exploring various use cases of Numpy
  • Hands-on with huge data using Numpy
2 Hours
Day 4

Pandas & Matplotlib Libraries

  • Fundamentals of pandas
  • Data frames and their operations
  • .csv .xml and various files data import
  • Data extraction, update and export
  • Fundamentals of Matplotlib
  • Various 2D & 3D graphs
  • Data visualisation in various types of graphs
  • Basics of Data Analytics
2 Hours
Day 5

Computer Vision & OpenCV Library

  • Fundamentals of Computer Vision.
  • Image Processing using Python.
  • OpenCV library for various data operations.
  • Working with live data.
  • Computer Vision for various fields like AR, VR, ML etc.
  • Morphological operations and Image Filtering & ROI Extractions.
  • Color Marker Detection.
    Project – Data Analytics using Python
2 Hours
Day 6

Machine Learning algorithms & Mathematics

  • What’s an Algorithm and Machine Learning Implementation
  • Various ML algos and their mathematics
  • Categories of Machine Learning (Supervised & Unsupervised &Reinforcemenet)
  • Classification, Regression & Clustering in ML
  • Decision Tree, Naive Bayes,KNN,SVM,Haar Classifiers
  • Linear & Logistic Regression
  • K-Means Clustering
  • Neural Network – ANN, CNN & RNN

Scikit-Learn Library

  • Fundamentals of the library
  • Various commands and their usecase

Working with the Algos…

Classifiers

  • What is Decision Tree?
  • Implementation & its Prediction Experience
  • Applying algo on various data sets
  • ML with Decision Tree Results accuracy
2 Hours
Day 7

Naive Bayes

  • A probability of Various Events
  • Bayes Theorem
  • Practise lab with DecisionTreealgo and number of examples
  • Training data with python using Naive Bayes
  • Deep dive with UCI
  • Lab session for loading data from different apis
  • Detecting data from numpy and converting for training and testing data
  • Exercise with ML and others framework
2 Hours
Day 8

ML Continued with Real Data set

  • Introduction to iris datasets
  • Understanding iris datasets
  • Modifying and loading with pandas
  • Separating data with numpy
  • Training classifier
  • Algo data process view
  • Decision Tree & Naive Bayes understanding & Results Comparisions
2 Hours
Day 9

K NearestNeighbours – KNN algo

  • Understanding the Mathematics and working of KNN
  • Implementing KNN by your Own
  • Apply your own designed KNN on real datasets
  • Comparing Designed KNN Results with Sklearn implementations
  • Applications of KNN
2 Hours
Day 10

Regression (Linear Regression)

  • Understanding functioning of the Algo and Its Mathematics
  • Implementing algo and applying datasets to it
  • Difference between Regression and Classification
  • Working with the real datasets
  • Stock exchange/GDP/Growth of the company analysis
  • Writing various codes upon various datasets
2 Hours
Day 11

SVM (Support Vector Machine)

  • Support Vector Classifier and Regression
  • Understanding the functioning of the Algo and Its Mathematics
  • Understanding Hyperplanes and its various internal parameters
  • Implementing algo and applying datasets to it
  • Difference between Regression and Classification
  • Working with the real datasets
  • Writing various codes upon various datasets
2 Hours
Day 12

Clustering (K-Means)

  • Unsupervised Learning.
  • Features and data vectors visualisation.
  • Various steps of algo implementation.
  • Understanding of Clusters and various types of Clustering.
  • Applying K-Means on datasets and their practical use cases.
  • Applications of Clustering and the algorithm
2 Hours
Day 13

Neural Network (NN)

  • What’s a Neural Network?
  • Various Structures of NN
  • Understanding Fundamentals and Various parameters of NN
  • ANN,CNN and RNN
  • Deep Dive with the Implementaion of NN on various datasets
  • Applying CNN on Images
  • Applications and its complexities over other algorithms
    Project:- Smart Machine Learning System
2 Hours
Day 14Objects Detections

  • Image processing and it’s various features for detection
  • Haar Classifier and its fuctioning behind
  • Cascading of features in Algorithm
  • Implementation of Haar Classifier on different image datasets
  • Realtime Object Detection
    Project:-Object Detection System
2 Hours
Day 15

Tensorflow

  • Fundamentals of tensorflow
  • What’s tensor and its flow graphs
  • Datatypes and Data Optimizers
  • Understanding Tensorflow from basics
  • Implementing usecase using tensorflow
  • Working on Realtime problem with Tensorflow and writing code for that
2 Hours
Day 16

Objects Recognitions

  • Understanding of Features of Objects for Recognitions
  • Working with Face Recognition Library
  • Recognition encodings
  • Various matching techniques for Recognition
  • Working on improving Efficiency of the code

Project:- Face Recognition System
Project:- Biometric Advance Attendance System

2 Hours
Day 17Projects Continued
Project:- Smart Music App using ML & Python
Project:- Building Security System
2 Hours
Day 18

Communication Protocol using Python

  • Various communication protocols for networking
  • Networking with MQTT
  • MQTT implementation with python over the internet
  • Worldwide data communication and analysis
    Project: – Auto Chat Bot using ML
2 Hours
Day 19API Integration with Python

  • What is an API?
  • What is Cloud & its Connections with Python?
  • Google Python Libraries
  • Speech Recognition
  • Text to Speech Conversion
  • Speech Recognition Exceptions
  • Various API’s Integration for ML
    Project:- Design & Development of your Personal Assistant
2 Hours
Day 20

ML over Cloud

  • Various cloud platforms for ML
  • Open Source Cloud for Features engineering
  • Various features for a person analysis
  • Registration and deletion of data over the cloud
  • Recognising images over cloud
    Project:- Gender & Expressions recognition system over clouds
2 Hours
Day 21

Weather & Other API’s

  • Various weather API’s
  • Data extraction from the raw weather information
  • Other API’s for data extraction from Web
  • Web Scrapping using network libraries in python
  • Data extraction from Zomato/Ola/Amazon or other such big online platform
    Project:- Smart Weather App using ML & Python
2 Hours
Day 22

Natural Language Processing

  • Concepts of Natural Language processing
  • NLP libraries in Python
  • Working with NLTK
  • Words Extractions from the text
  • Sentiment Analysis concepts
    Project:- Smart Talking System using ML
2 Hours
Day 23

Keras Library and Its Implementation

  • Understanding wide range of Keras library
  • Keras and its various structures for images
  • Backend tensorflow mechanism for pattern recognition
  • Deep learning models and their formations
  • Deep learning models use case with ML for Expression Recognition

Project:- Facial Expression Recognition System

2 Hours
Day 24

Deep Learning Concepts

  • Understanding Deep Learning
  • Various Concepts of Deep Learning
  • How does this model work?
  • How to prepare your own Models?
  • Various problems to work with Deep Learning
    Project:- Preparation of Self Deep Learning Models using Custom datasets
2 Hours
Day 25Project Completion
Query Session
2 Hours
Machine Learning (Advanced)
Course:Machine Learning
Certification By:TechieNest, An ISO 9001:2008 Certified Company
Study Material:Book free to each participant (Soft Copy)
FeeINR 12,500/- + Taxes
Duration50 Days/ 100 Hours

Course Details


Machine Learning
DAYTOPICDURATION
Day 1

Machine Learning

  • Introduction to machine learning
  • Understanding the need
  • Understanding Big data and machine learning
  • Running machine learning under Linux platform
  • Introduction to Redhat Enterprise Linux
  • Why Linux is important for machine learning with respect to future
  • Role of Python and R programming in this domain
  • Basic Introduction of Python syntax and programming logic
  • Deep dive with Supervised, Unsupervised and Reinforcement learning
  • Algo discussion with use case
  • Popular machine learning framework like sensor flow , scikit-learn
2 Hours
Day 2

Advance Python programming and its use case

  • Basic of python and why python for machine learning
  • Installation of software and libraries on different OS.
  • Revising python concepts
  • Advance python programming
  • Hands-on with Python standard libraries
  • GITHUB exposure
2 Hours
Day 3Data Science Libraries

  • Understanding & use of Various Open source libraries
  • Importing various modules with different methods
  • File handling with Python
  • Working with Numpy
  • Data types and its various Numerical operations
  • Exploring various use cases of Numpy
  • Hands-on with huge data using Numpy
2 Hours
Day 4

Pandas & Matplotlib Libraries

  • Fundamentals of pandas
  • Data frames and their operations
  • .csv .xml and various files data import
  • Data extraction, update and export
  • Fundamentals of Matplotlib
  • Various 2D & 3D graphs
  • Data visualisation in various types of graphs
  • Basics of Data Analytics
2 Hours
Day 5

Computer Vision & OpenCV Library

  • Fundamentals of Computer Vision.
  • Image Processing using Python.
  • OpenCV library for various data operations.
  • Working with live data.
  • Computer Vision for various fields like AR, VR, ML etc.
  • Morphological operations and Image Filtering & ROI Extractions.
  • Color Marker Detection.
    Project – Data Analytics using Python
2 Hours
Day 6

Machine Learning algorithms & Mathematics

  • What’s an Algorithm and Machine Learning Implementation
  • Various ML algos and their mathematics
  • Categories of Machine Learning (Supervised & Unsupervised &Reinforcemenet)
  • Classification, Regression & Clustering in ML
  • Decision Tree, Naive Bayes,KNN,SVM,Haar Classifiers
  • Linear & Logistic Regression
  • K-Means Clustering
  • Neural Network – ANN, CNN & RNN

Scikit-Learn Library

  • Fundamentals of the library
  • Various commands and their usecase

Working with the Algos…

Classifiers

  • What is Decision Tree?
  • Implementation & its Prediction Experience
  • Applying algo on various data sets
  • ML with Decision Tree Results accuracy
2 Hours
Day 7

Naive Bayes

  • A probability of Various Events
  • Bayes Theorem
  • Practise lab with DecisionTreealgo and number of examples
  • Training data with python using Naive Bayes
  • Deep dive with UCI
  • Lab session for loading data from different apis
  • Detecting data from numpy and converting for training and testing data
  • Exercise with ML and others framework
2 Hours
Day 8

ML Continued with Real Data set

  • Introduction to iris datasets
  • Understanding iris datasets
  • Modifying and loading with pandas
  • Separating data with numpy
  • Training classifier
  • Algo data process view
  • Decision Tree & Naive Bayes understanding & Results Comparisions
2 Hours
Day 9

K NearestNeighbours – KNN algo

  • Understanding the Mathematics and working of KNN
  • Implementing KNN by your Own
  • Apply your own designed KNN on real datasets
  • Comparing Designed KNN Results with Sklearn implementations
  • Applications of KNN
2 Hours
Day 10

Regression (Linear Regression)

  • Understanding functioning of the Algo and Its Mathematics
  • Implementing algo and applying datasets to it
  • Difference between Regression and Classification
  • Working with the real datasets
  • Stock exchange/GDP/Growth of the company analysis
  • Writing various codes upon various datasets
2 Hours
Day 11

SVM (Support Vector Machine)

  • Support Vector Classifier and Regression
  • Understanding the functioning of the Algo and Its Mathematics
  • Understanding Hyperplanes and its various internal parameters
  • Implementing algo and applying datasets to it
  • Difference between Regression and Classification
  • Working with the real datasets
  • Writing various codes upon various datasets
2 Hours
Day 12

Clustering (K-Means)

  • Unsupervised Learning.
  • Features and data vectors visualisation.
  • Various steps of algo implementation.
  • Understanding of Clusters and various types of Clustering.
  • Applying K-Means on datasets and their practical use cases.
  • Applications of Clustering and the algorithm
2 Hours
Day 13

Neural Network (NN)

  • What’s a Neural Network?
  • Various Structures of NN
  • Understanding Fundamentals and Various parameters of NN
  • ANN,CNN and RNN
  • Deep Dive with the Implementaion of NN on various datasets
  • Applying CNN on Images
  • Applications and its complexities over other algorithms
    Project:- Smart Machine Learning System
2 Hours
Day 14Objects Detections

  • Image processing and it’s various features for detection
  • Haar Classifier and its fuctioning behind
  • Cascading of features in Algorithm
  • Implementation of Haar Classifier on different image datasets
  • Realtime Object Detection
    Project:-Object Detection System
2 Hours
Day 15

Tensorflow

  • Fundamentals of tensorflow
  • What’s tensor and its flow graphs
  • Datatypes and Data Optimizers
  • Understanding Tensorflow from basics
  • Implementing usecase using tensorflow
  • Working on Realtime problem with Tensorflow and writing code for that
2 Hours
Day 16

Objects Recognitions

  • Understanding of Features of Objects for Recognitions
  • Working with Face Recognition Library
  • Recognition encodings
  • Various matching techniques for Recognition
  • Working on improving Efficiency of the code

Project:- Face Recognition System
Project:- Biometric Advance Attendance System

2 Hours
Day 17Projects Continued
Project:- Smart Music App using ML & Python
Project:- Building Security System
2 Hours
Day 18

Communication Protocol using Python

  • Various communication protocols for networking
  • Networking with MQTT
  • MQTT implementation with python over the internet
  • Worldwide data communication and analysis
    Project: – Auto Chat Bot using ML
2 Hours
Day 19API Integration with Python

  • What is an API?
  • What is Cloud & its Connections with Python?
  • Google Python Libraries
  • Speech Recognition
  • Text to Speech Conversion
  • Speech Recognition Exceptions
  • Various API’s Integration for ML
    Project:- Design & Development of your Personal Assistant
2 Hours
Day 20

ML over Cloud

  • Various cloud platforms for ML
  • Open Source Cloud for Features engineering
  • Various features for a person analysis
  • Registration and deletion of data over the cloud
  • Recognising images over cloud
    Project:- Gender & Expressions recognition system over clouds
2 Hours
Day 21

Weather & Other API’s

  • Various weather API’s
  • Data extraction from the raw weather information
  • Other API’s for data extraction from Web
  • Web Scrapping using network libraries in python
  • Data extraction from Zomato/Ola/Amazon or other such big online platform
    Project:- Smart Weather App using ML & Python
2 Hours
Day 22

Natural Language Processing

  • Concepts of Natural Language processing
  • NLP libraries in Python
  • Working with NLTK
  • Words Extractions from the text
  • Sentiment Analysis concepts
    Project:- Smart Talking System using ML
2 Hours
Day 23

Keras Library and Its Implementation

  • Understanding wide range of Keras library
  • Keras and its various structures for images
  • Backend tensorflow mechanism for pattern recognition
  • Deep learning models and their formations
  • Deep learning models use case with ML for Expression Recognition

Project:- Facial Expression Recognition System

2 Hours
Day 24

Deep Learning Concepts

  • Understanding Deep Learning
  • Various Concepts of Deep Learning
  • How does this model work?
  • How to prepare your own Models?
  • Various problems to work with Deep Learning
    Project:- Preparation of Self Deep Learning Models using Custom datasets
2 Hours
Day 25Project Completion
Query Session
2 Hours
Day 26Advance Data Science and Analytics

  • Advance tools and libraries for Data Science
  • Data Mining, Custom Data Formation
  • Data Storage and Visualisation
  • Custom software and implementations
  • SQL databases and connections through python
    Software installations
2 Hours
Day 27Seaborn Library for Graphical Data Visualisation

  • Plotting Data
  • Various Graphs and analysis
  • Various Tactics and methods of graphs plotting
  • Real Data sets visualisation
  • Seaborn verses Matplotlib
  • Seaborn Verses Tableau
    Project:- Data Analyics Software Development
2 Hours
Day 28Data Science with R

  • Basics of R Programming
  • Data Types and its usecases in data science
  • Functions and modules in R for data science
  • Data Plotting using R Language
  • Data Visualisation and Analysis with R
2 Hours
Day 29Image Data Analysis, Extraction and Manupulations

  • Working with Python and Open cv
  • Image features analysis and morphological processing
  • Various Filters to better ROI and its pixels calulations
  • Transformations in images
  • Analysis on various types of images
    Project:- Image content Deep Analysis
Day 30Machine Learning with Data Science & Advanced Computer Vision

  • Text analysis in the images
  • Words and characters ROI generation
  • Various formulations for image data training sets
  • Machine Learning with extracted image data
    Project:- Vehicle Number plate Detection
    Project:- Image to Text Conversions in Offline mode
2 Hours
Day 31Deep Learning

  • What’s Deep Learning and differ from Machine Learning?
  • Deep structured learning
  • Deep neural Network
  • Machine training and models
  • Deep dive with Neural Network
2 Hours
Day 32Artificial Neural Network (ANN)

  • What is a Neuron in Technology?
  • Deep learning using ANN
  • ANN fundamentals and its network parameters
  • Propogations
  • Feedback mechanism
  • Mathematical Approach and Analysis of ANN
  • ANN Implementation on various data sets
2 Hours
Day 33Convolutional Neural Network (CNN)

  • Basics of CNN
  • CNN Vs ANN
  • CNN for Images
  • Deep learning using CNN
  • CNN Mathematics and its usecase
  • CNN implementation using Python
  • Image Recognitions
2 Hours
Day 34Recurrent Neural Network (RNN)

  • Basics of RNN
  • RNN Vs CNN Vs ANN
  • Problem solutions using RNN
  • Deep learning using RNN
  • Mathematics behind RNN
  • RNN Implementation using Python
2 Hours
Day 35Deep learning Models Preparation

  • Various Architecture for DL models
  • Custom Datasets formations
  • Positive and Negative Data
  • Informative Data Extrations
    Preparing your own DL Models for Machine Learning
2 Hours
Day 36Tensorflow 2.0

  • Advance of tensorflow
  • Fundamentals of tensorflow and implementation with Python
  • Deep Learning Models with Tensorflow
2 Hours
Day 37Deep Learning using Keras

  • Fundamentals of the library
  • Deep Dive with Keras
  • Keras implementation using python on images
  • DL models preparation using Keras and Tensorflow
2 Hours
Day 38Keras Continued…2 Hours
Day 39Caffe Deep Learning Framework

  • Fundamentals of the library
  • Caffe implementation using Python
2 Hours
Day 40Caffe Continued…2 Hours
Day 41Pytorch Deep Learning Framework

  • Pytorch a Python Library
  • Fundamentals of the library
  • Pytorch Vs Caffe Vs Tensorflow
  • DL using Pytorch
2 Hours
Day 42Pytorch Continued…2 Hours
Day 43Deep Learning Architecture with theano

  • What is Theano?
  • Theano Vs others
  • Working and implementation DL using Theano
2 Hours
Day 44Reinforcement Machine Learning

  • Q-Learning
  • Implementation of Q-Learning with Python
2 Hours
Day 45Natural Language Processing (NLP) with Deep Learning

  • Word and sentence extraxtions
  • Meaningful clusters formations
  • NLP using Python
2 Hours
Day 46Data Science & Machine Learning on AWS

  • AWS cloud machine learning architecture
  • IaaS Vs SaaS Vs PaaS
  • EC2 & S3 on AWS
  • Data visualisation and ML algos on AWS
2 Hours
Day 47Projects Development-

Project:- Advance Face Recogntion System
Project:- Self Learning System
Project:- Jarvis 2.0

2 Hours
Day 48Project:- ML for Emotions Detection
Project:- ML in AR & VR
2 Hours
Day 49Project:- ML in Automation & Security
Project:- ML in Education
Project:- ML in Agriculture
2 Hours
Day 50Project:- Advance Recommendation System using ML2 Hours

Step 1

Register online for any desired course, duration & location of your training course & obtain a Registration-ID. Registration-ID is a Unique Registration Number which is generated by our system after successful registration for training A student can have multiple IDs for multiple courses & batches. It is displayed while successful registration and it is also mailed to you immediately after registration by our server. if you don’t find it in your mail then, please check your SPAM folder or junk folder of your mail ID.

Step 2

Please deposit your Course fee to any one of our payment gateway/ Bank Account/ paytm.

Payment Gateway link: PayUmoney gateway

Bank Account Details

TechieNest Pvt Ltd

Indusind Bank Limited

Malviya Nagar
Jaipur ( Rajasthan)
Account No : 201000689491
IFSC: INDB0000592

Paytm Number9251494002

Step 3

Update us regarding your fee payment by sending picture/scan copy of bank receipt to: training@techienest.in and you will receive a confirmation mail on your mail id.

When someone says yes you can do it….it means you can achieve it and when you decide to take an action we come with the surprising offers:

1. Group Discount:

Offer code: TNGD-5
Offer code: TNGD-10
Offer code: TNGD-15

  • If a group size is of: 5 -10 then 5% discount on training
  • 10-20 then 10% discount on training
  • 20 and above then 15% discount on training
2. Referral Offer:

Offer code: TNR3
Offer code: TNR5

  • 3% additional discount to the person who is referring
  • 5% additional discount to the one who is being referred
3. For Former students up to 15% off:

Offer code: TNFS15

  • There will be 15% discount on students who already did training
4. Previous Workshop attended students 5% off:

Offer code: TNPW5

5. 5% additional Discount for Campus Ambassador:

Offer code: TNA5

Certification

All participants will get Certificate from TechieNest Pvt. Ltd. in association with Aavriti’18 IIT Bombay

Why TechieNest

  • Vast experience of having conducted Big Outreach Workshop collaborating with over 300+ colleges in all over India including IIT Bombay, IIT Hyderabad, IIT Bhubaneswar, IIT Jodhpur, IIT Mandi, NIT Raipur, MNIT Jaipur, MANIT Bhopal, NIT Jalandhar, NIT Patna, NIT Srinagar, IIIT Kalyani, BITS Pilani and likewise.
  • Trained more than 20,000 students in the field of EMBEDDED SYSTEMS & ROBOTICS, MATLAB & Machine Vision, Internet of Things, PLC_SCADA, PYTHON, C/C++, Andriod, VLSI & VHDL, JAVA and such top notch courses.
  • Our trainers are efficient in Raspberry pi, Arduino, PLCs, etc. which forms essential hardware in Electronic Industries nowadays.
  • Outreach workshop partner of Sanchaar-Wissenaire’18, IIT Bhubaneswar, 2017-18
  • Zonal workshop partner of Techkriti’18 IIT Kanpur, 2017-2018
  • Outreach workshop partner of Techfest’15 IIT Bombay & Techfest’16 IIT Bombay
  • Zonal workshop partner of Techkriti’17 IIT Kanpur, 2016-2017
  • Outreach workshop & Training partner of nVision’17 IIT Hyderabad, 2016-17
  • Outreach workshop partner of Ignus’17 IIT Jodhpur, 2016-17
  • AIRC’18 (All India Robotics Championship) in association with Techkriti’18 IIT Kanpur.
  • AIRC’17 (All India Robotics Championship) in association with nVision’17 IIT Hyderabad, 2016-17
  • Offering Project Based Training, Projects on Demand, Corporate Projects, Commercial Projects, and Consultancy in Engineering Projects.
    Dedicated 24×7 R&D lab.
  • Trained over 50+ international students in TechieNest Technology Transfer Program 2014-15.
  • TechieNest has Research Engineers having excellent research aptitude, teaching pedagogy who illustrates their finding through practical demos during workshop/training.
  • Manufacturer of Electronic products delivering the same across the country.

Training Kit- will be updated soon

Course
Machine Learning (Regular)
Course:Machine Learning
Certification By:TechieNest, An ISO 9001:2008 Certified Company
Study Material:Book free to each participant (Soft Copy)
FeeINR 9,500/- + Taxes
Duration1 Months/ 50 Hours

Course Details


Machine Learning
DAYTOPICDURATION
Day 1

Machine Learning

  • Introduction to machine learning
  • Understanding the need
  • Understanding Big data and machine learning
  • Running machine learning under Linux platform
  • Introduction to Redhat Enterprise Linux
  • Why Linux is important for machine learning with respect to future
  • Role of Python and R programming in this domain
  • Basic Introduction of Python syntax and programming logic
  • Deep dive with Supervised, Unsupervised and Reinforcement learning
  • Algo discussion with use case
  • Popular machine learning framework like sensor flow , scikit-learn
2 Hours
Day 2

Advance Python programming and its use case

  • Basic of python and why python for machine learning
  • Installation of software and libraries on different OS.
  • Revising python concepts
  • Advance python programming
  • Hands-on with Python standard libraries
  • GITHUB exposure
2 Hours
Day 3Data Science Libraries

  • Understanding & use of Various Open source libraries
  • Importing various modules with different methods
  • File handling with Python
  • Working with Numpy
  • Data types and its various Numerical operations
  • Exploring various use cases of Numpy
  • Hands-on with huge data using Numpy
2 Hours
Day 4

Pandas & Matplotlib Libraries

  • Fundamentals of pandas
  • Data frames and their operations
  • .csv .xml and various files data import
  • Data extraction, update and export
  • Fundamentals of Matplotlib
  • Various 2D & 3D graphs
  • Data visualisation in various types of graphs
  • Basics of Data Analytics
2 Hours
Day 5

Computer Vision & OpenCV Library

  • Fundamentals of Computer Vision.
  • Image Processing using Python.
  • OpenCV library for various data operations.
  • Working with live data.
  • Computer Vision for various fields like AR, VR, ML etc.
  • Morphological operations and Image Filtering & ROI Extractions.
  • Color Marker Detection.
    Project – Data Analytics using Python
2 Hours
Day 6

Machine Learning algorithms & Mathematics

  • What’s an Algorithm and Machine Learning Implementation
  • Various ML algos and their mathematics
  • Categories of Machine Learning (Supervised & Unsupervised &Reinforcemenet)
  • Classification, Regression & Clustering in ML
  • Decision Tree, Naive Bayes,KNN,SVM,Haar Classifiers
  • Linear & Logistic Regression
  • K-Means Clustering
  • Neural Network – ANN, CNN & RNN

Scikit-Learn Library

  • Fundamentals of the library
  • Various commands and their usecase

Working with the Algos…

Classifiers

  • What is Decision Tree?
  • Implementation & its Prediction Experience
  • Applying algo on various data sets
  • ML with Decision Tree Results accuracy
2 Hours
Day 7

Naive Bayes

  • A probability of Various Events
  • Bayes Theorem
  • Practise lab with DecisionTreealgo and number of examples
  • Training data with python using Naive Bayes
  • Deep dive with UCI
  • Lab session for loading data from different apis
  • Detecting data from numpy and converting for training and testing data
  • Exercise with ML and others framework
2 Hours
Day 8

ML Continued with Real Data set

  • Introduction to iris datasets
  • Understanding iris datasets
  • Modifying and loading with pandas
  • Separating data with numpy
  • Training classifier
  • Algo data process view
  • Decision Tree & Naive Bayes understanding & Results Comparisions
2 Hours
Day 9

K NearestNeighbours – KNN algo

  • Understanding the Mathematics and working of KNN
  • Implementing KNN by your Own
  • Apply your own designed KNN on real datasets
  • Comparing Designed KNN Results with Sklearn implementations
  • Applications of KNN
2 Hours
Day 10

Regression (Linear Regression)

  • Understanding functioning of the Algo and Its Mathematics
  • Implementing algo and applying datasets to it
  • Difference between Regression and Classification
  • Working with the real datasets
  • Stock exchange/GDP/Growth of the company analysis
  • Writing various codes upon various datasets
2 Hours
Day 11

SVM (Support Vector Machine)

  • Support Vector Classifier and Regression
  • Understanding the functioning of the Algo and Its Mathematics
  • Understanding Hyperplanes and its various internal parameters
  • Implementing algo and applying datasets to it
  • Difference between Regression and Classification
  • Working with the real datasets
  • Writing various codes upon various datasets
2 Hours
Day 12

Clustering (K-Means)

  • Unsupervised Learning.
  • Features and data vectors visualisation.
  • Various steps of algo implementation.
  • Understanding of Clusters and various types of Clustering.
  • Applying K-Means on datasets and their practical use cases.
  • Applications of Clustering and the algorithm
2 Hours
Day 13

Neural Network (NN)

  • What’s a Neural Network?
  • Various Structures of NN
  • Understanding Fundamentals and Various parameters of NN
  • ANN,CNN and RNN
  • Deep Dive with the Implementaion of NN on various datasets
  • Applying CNN on Images
  • Applications and its complexities over other algorithms
    Project:- Smart Machine Learning System
2 Hours
Day 14Objects Detections

  • Image processing and it’s various features for detection
  • Haar Classifier and its fuctioning behind
  • Cascading of features in Algorithm
  • Implementation of Haar Classifier on different image datasets
  • Realtime Object Detection
    Project:-Object Detection System
2 Hours
Day 15

Tensorflow

  • Fundamentals of tensorflow
  • What’s tensor and its flow graphs
  • Datatypes and Data Optimizers
  • Understanding Tensorflow from basics
  • Implementing usecase using tensorflow
  • Working on Realtime problem with Tensorflow and writing code for that
2 Hours
Day 16

Objects Recognitions

  • Understanding of Features of Objects for Recognitions
  • Working with Face Recognition Library
  • Recognition encodings
  • Various matching techniques for Recognition
  • Working on improving Efficiency of the code

Project:- Face Recognition System
Project:- Biometric Advance Attendance System

2 Hours
Day 17Projects Continued
Project:- Smart Music App using ML & Python
Project:- Building Security System
2 Hours
Day 18

Communication Protocol using Python

  • Various communication protocols for networking
  • Networking with MQTT
  • MQTT implementation with python over the internet
  • Worldwide data communication and analysis
    Project: – Auto Chat Bot using ML
2 Hours
Day 19API Integration with Python

  • What is an API?
  • What is Cloud & its Connections with Python?
  • Google Python Libraries
  • Speech Recognition
  • Text to Speech Conversion
  • Speech Recognition Exceptions
  • Various API’s Integration for ML
    Project:- Design & Development of your Personal Assistant
2 Hours
Day 20

ML over Cloud

  • Various cloud platforms for ML
  • Open Source Cloud for Features engineering
  • Various features for a person analysis
  • Registration and deletion of data over the cloud
  • Recognising images over cloud
    Project:- Gender & Expressions recognition system over clouds
2 Hours
Day 21

Weather & Other API’s

  • Various weather API’s
  • Data extraction from the raw weather information
  • Other API’s for data extraction from Web
  • Web Scrapping using network libraries in python
  • Data extraction from Zomato/Ola/Amazon or other such big online platform
    Project:- Smart Weather App using ML & Python
2 Hours
Day 22

Natural Language Processing

  • Concepts of Natural Language processing
  • NLP libraries in Python
  • Working with NLTK
  • Words Extractions from the text
  • Sentiment Analysis concepts
    Project:- Smart Talking System using ML
2 Hours
Day 23

Keras Library and Its Implementation

  • Understanding wide range of Keras library
  • Keras and its various structures for images
  • Backend tensorflow mechanism for pattern recognition
  • Deep learning models and their formations
  • Deep learning models use case with ML for Expression Recognition

Project:- Facial Expression Recognition System

2 Hours
Day 24

Deep Learning Concepts

  • Understanding Deep Learning
  • Various Concepts of Deep Learning
  • How does this model work?
  • How to prepare your own Models?
  • Various problems to work with Deep Learning
    Project:- Preparation of Self Deep Learning Models using Custom datasets
2 Hours
Day 25Project Completion
Query Session
2 Hours
Machine Learning (Advanced)
Course:Machine Learning
Certification By:TechieNest, An ISO 9001:2008 Certified Company
Study Material:Book free to each participant (Soft Copy)
FeeINR 12,500/- + Taxes
Duration50 Days/ 100 Hours

Course Details


Machine Learning
DAYTOPICDURATION
Day 1

Machine Learning

  • Introduction to machine learning
  • Understanding the need
  • Understanding Big data and machine learning
  • Running machine learning under Linux platform
  • Introduction to Redhat Enterprise Linux
  • Why Linux is important for machine learning with respect to future
  • Role of Python and R programming in this domain
  • Basic Introduction of Python syntax and programming logic
  • Deep dive with Supervised, Unsupervised and Reinforcement learning
  • Algo discussion with use case
  • Popular machine learning framework like sensor flow , scikit-learn
2 Hours
Day 2

Advance Python programming and its use case

  • Basic of python and why python for machine learning
  • Installation of software and libraries on different OS.
  • Revising python concepts
  • Advance python programming
  • Hands-on with Python standard libraries
  • GITHUB exposure
2 Hours
Day 3Data Science Libraries

  • Understanding & use of Various Open source libraries
  • Importing various modules with different methods
  • File handling with Python
  • Working with Numpy
  • Data types and its various Numerical operations
  • Exploring various use cases of Numpy
  • Hands-on with huge data using Numpy
2 Hours
Day 4

Pandas & Matplotlib Libraries

  • Fundamentals of pandas
  • Data frames and their operations
  • .csv .xml and various files data import
  • Data extraction, update and export
  • Fundamentals of Matplotlib
  • Various 2D & 3D graphs
  • Data visualisation in various types of graphs
  • Basics of Data Analytics
2 Hours
Day 5

Computer Vision & OpenCV Library

  • Fundamentals of Computer Vision.
  • Image Processing using Python.
  • OpenCV library for various data operations.
  • Working with live data.
  • Computer Vision for various fields like AR, VR, ML etc.
  • Morphological operations and Image Filtering & ROI Extractions.
  • Color Marker Detection.
    Project – Data Analytics using Python
2 Hours
Day 6

Machine Learning algorithms & Mathematics

  • What’s an Algorithm and Machine Learning Implementation
  • Various ML algos and their mathematics
  • Categories of Machine Learning (Supervised & Unsupervised &Reinforcemenet)
  • Classification, Regression & Clustering in ML
  • Decision Tree, Naive Bayes,KNN,SVM,Haar Classifiers
  • Linear & Logistic Regression
  • K-Means Clustering
  • Neural Network – ANN, CNN & RNN

Scikit-Learn Library

  • Fundamentals of the library
  • Various commands and their usecase

Working with the Algos…

Classifiers

  • What is Decision Tree?
  • Implementation & its Prediction Experience
  • Applying algo on various data sets
  • ML with Decision Tree Results accuracy
2 Hours
Day 7

Naive Bayes

  • A probability of Various Events
  • Bayes Theorem
  • Practise lab with DecisionTreealgo and number of examples
  • Training data with python using Naive Bayes
  • Deep dive with UCI
  • Lab session for loading data from different apis
  • Detecting data from numpy and converting for training and testing data
  • Exercise with ML and others framework
2 Hours
Day 8

ML Continued with Real Data set

  • Introduction to iris datasets
  • Understanding iris datasets
  • Modifying and loading with pandas
  • Separating data with numpy
  • Training classifier
  • Algo data process view
  • Decision Tree & Naive Bayes understanding & Results Comparisions
2 Hours
Day 9

K NearestNeighbours – KNN algo

  • Understanding the Mathematics and working of KNN
  • Implementing KNN by your Own
  • Apply your own designed KNN on real datasets
  • Comparing Designed KNN Results with Sklearn implementations
  • Applications of KNN
2 Hours
Day 10

Regression (Linear Regression)

  • Understanding functioning of the Algo and Its Mathematics
  • Implementing algo and applying datasets to it
  • Difference between Regression and Classification
  • Working with the real datasets
  • Stock exchange/GDP/Growth of the company analysis
  • Writing various codes upon various datasets
2 Hours
Day 11

SVM (Support Vector Machine)

  • Support Vector Classifier and Regression
  • Understanding the functioning of the Algo and Its Mathematics
  • Understanding Hyperplanes and its various internal parameters
  • Implementing algo and applying datasets to it
  • Difference between Regression and Classification
  • Working with the real datasets
  • Writing various codes upon various datasets
2 Hours
Day 12

Clustering (K-Means)

  • Unsupervised Learning.
  • Features and data vectors visualisation.
  • Various steps of algo implementation.
  • Understanding of Clusters and various types of Clustering.
  • Applying K-Means on datasets and their practical use cases.
  • Applications of Clustering and the algorithm
2 Hours
Day 13

Neural Network (NN)

  • What’s a Neural Network?
  • Various Structures of NN
  • Understanding Fundamentals and Various parameters of NN
  • ANN,CNN and RNN
  • Deep Dive with the Implementaion of NN on various datasets
  • Applying CNN on Images
  • Applications and its complexities over other algorithms
    Project:- Smart Machine Learning System
2 Hours
Day 14Objects Detections

  • Image processing and it’s various features for detection
  • Haar Classifier and its fuctioning behind
  • Cascading of features in Algorithm
  • Implementation of Haar Classifier on different image datasets
  • Realtime Object Detection
    Project:-Object Detection System
2 Hours
Day 15

Tensorflow

  • Fundamentals of tensorflow
  • What’s tensor and its flow graphs
  • Datatypes and Data Optimizers
  • Understanding Tensorflow from basics
  • Implementing usecase using tensorflow
  • Working on Realtime problem with Tensorflow and writing code for that
2 Hours
Day 16

Objects Recognitions

  • Understanding of Features of Objects for Recognitions
  • Working with Face Recognition Library
  • Recognition encodings
  • Various matching techniques for Recognition
  • Working on improving Efficiency of the code

Project:- Face Recognition System
Project:- Biometric Advance Attendance System

2 Hours
Day 17Projects Continued
Project:- Smart Music App using ML & Python
Project:- Building Security System
2 Hours
Day 18

Communication Protocol using Python

  • Various communication protocols for networking
  • Networking with MQTT
  • MQTT implementation with python over the internet
  • Worldwide data communication and analysis
    Project: – Auto Chat Bot using ML
2 Hours
Day 19API Integration with Python

  • What is an API?
  • What is Cloud & its Connections with Python?
  • Google Python Libraries
  • Speech Recognition
  • Text to Speech Conversion
  • Speech Recognition Exceptions
  • Various API’s Integration for ML
    Project:- Design & Development of your Personal Assistant
2 Hours
Day 20

ML over Cloud

  • Various cloud platforms for ML
  • Open Source Cloud for Features engineering
  • Various features for a person analysis
  • Registration and deletion of data over the cloud
  • Recognising images over cloud
    Project:- Gender & Expressions recognition system over clouds
2 Hours
Day 21

Weather & Other API’s

  • Various weather API’s
  • Data extraction from the raw weather information
  • Other API’s for data extraction from Web
  • Web Scrapping using network libraries in python
  • Data extraction from Zomato/Ola/Amazon or other such big online platform
    Project:- Smart Weather App using ML & Python
2 Hours
Day 22

Natural Language Processing

  • Concepts of Natural Language processing
  • NLP libraries in Python
  • Working with NLTK
  • Words Extractions from the text
  • Sentiment Analysis concepts
    Project:- Smart Talking System using ML
2 Hours
Day 23

Keras Library and Its Implementation

  • Understanding wide range of Keras library
  • Keras and its various structures for images
  • Backend tensorflow mechanism for pattern recognition
  • Deep learning models and their formations
  • Deep learning models use case with ML for Expression Recognition

Project:- Facial Expression Recognition System

2 Hours
Day 24

Deep Learning Concepts

  • Understanding Deep Learning
  • Various Concepts of Deep Learning
  • How does this model work?
  • How to prepare your own Models?
  • Various problems to work with Deep Learning
    Project:- Preparation of Self Deep Learning Models using Custom datasets
2 Hours
Day 25Project Completion
Query Session
2 Hours
Day 26Advance Data Science and Analytics

  • Advance tools and libraries for Data Science
  • Data Mining, Custom Data Formation
  • Data Storage and Visualisation
  • Custom software and implementations
  • SQL databases and connections through python
    Software installations
2 Hours
Day 27Seaborn Library for Graphical Data Visualisation

  • Plotting Data
  • Various Graphs and analysis
  • Various Tactics and methods of graphs plotting
  • Real Data sets visualisation
  • Seaborn verses Matplotlib
  • Seaborn Verses Tableau
    Project:- Data Analyics Software Development
2 Hours
Day 28Data Science with R

  • Basics of R Programming
  • Data Types and its usecases in data science
  • Functions and modules in R for data science
  • Data Plotting using R Language
  • Data Visualisation and Analysis with R
2 Hours
Day 29Image Data Analysis, Extraction and Manupulations

  • Working with Python and Open cv
  • Image features analysis and morphological processing
  • Various Filters to better ROI and its pixels calulations
  • Transformations in images
  • Analysis on various types of images
    Project:- Image content Deep Analysis
Day 30Machine Learning with Data Science & Advanced Computer Vision

  • Text analysis in the images
  • Words and characters ROI generation
  • Various formulations for image data training sets
  • Machine Learning with extracted image data
    Project:- Vehicle Number plate Detection
    Project:- Image to Text Conversions in Offline mode
2 Hours
Day 31Deep Learning

  • What’s Deep Learning and differ from Machine Learning?
  • Deep structured learning
  • Deep neural Network
  • Machine training and models
  • Deep dive with Neural Network
2 Hours
Day 32Artificial Neural Network (ANN)

  • What is a Neuron in Technology?
  • Deep learning using ANN
  • ANN fundamentals and its network parameters
  • Propogations
  • Feedback mechanism
  • Mathematical Approach and Analysis of ANN
  • ANN Implementation on various data sets
2 Hours
Day 33Convolutional Neural Network (CNN)

  • Basics of CNN
  • CNN Vs ANN
  • CNN for Images
  • Deep learning using CNN
  • CNN Mathematics and its usecase
  • CNN implementation using Python
  • Image Recognitions
2 Hours
Day 34Recurrent Neural Network (RNN)

  • Basics of RNN
  • RNN Vs CNN Vs ANN
  • Problem solutions using RNN
  • Deep learning using RNN
  • Mathematics behind RNN
  • RNN Implementation using Python
2 Hours
Day 35Deep learning Models Preparation

  • Various Architecture for DL models
  • Custom Datasets formations
  • Positive and Negative Data
  • Informative Data Extrations
    Preparing your own DL Models for Machine Learning
2 Hours
Day 36Tensorflow 2.0

  • Advance of tensorflow
  • Fundamentals of tensorflow and implementation with Python
  • Deep Learning Models with Tensorflow
2 Hours
Day 37Deep Learning using Keras

  • Fundamentals of the library
  • Deep Dive with Keras
  • Keras implementation using python on images
  • DL models preparation using Keras and Tensorflow
2 Hours
Day 38Keras Continued…2 Hours
Day 39Caffe Deep Learning Framework

  • Fundamentals of the library
  • Caffe implementation using Python
2 Hours
Day 40Caffe Continued…2 Hours
Day 41Pytorch Deep Learning Framework

  • Pytorch a Python Library
  • Fundamentals of the library
  • Pytorch Vs Caffe Vs Tensorflow
  • DL using Pytorch
2 Hours
Day 42Pytorch Continued…2 Hours
Day 43Deep Learning Architecture with theano

  • What is Theano?
  • Theano Vs others
  • Working and implementation DL using Theano
2 Hours
Day 44Reinforcement Machine Learning

  • Q-Learning
  • Implementation of Q-Learning with Python
2 Hours
Day 45Natural Language Processing (NLP) with Deep Learning

  • Word and sentence extraxtions
  • Meaningful clusters formations
  • NLP using Python
2 Hours
Day 46Data Science & Machine Learning on AWS

  • AWS cloud machine learning architecture
  • IaaS Vs SaaS Vs PaaS
  • EC2 & S3 on AWS
  • Data visualisation and ML algos on AWS
2 Hours
Day 47Projects Development-

Project:- Advance Face Recogntion System
Project:- Self Learning System
Project:- Jarvis 2.0

2 Hours
Day 48Project:- ML for Emotions Detection
Project:- ML in AR & VR
2 Hours
Day 49Project:- ML in Automation & Security
Project:- ML in Education
Project:- ML in Agriculture
2 Hours
Day 50Project:- Advance Recommendation System using ML2 Hours
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Certification

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Why TechieNest

  • Vast experience of having conducted Big Outreach Workshop collaborating with over 300+ colleges in all over India including IIT Bombay, IIT Hyderabad, IIT Bhubaneswar, IIT Jodhpur, IIT Mandi, NIT Raipur, MNIT Jaipur, MANIT Bhopal, NIT Jalandhar, NIT Patna, NIT Srinagar, IIIT Kalyani, BITS Pilani and likewise.
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